Entropic Approach for Reduction of Amino Acid Alphabets

The primitive data for deducing the Miyazawa-Jernigan contact energy or BLOSUM score metrix are the pair frequency counts. Each amino acid corresponds to a distribution. Taking the Kullback-Leibler distance of two probability distributions as resemblance coefficient and relating cluster to mixed population, we perform cluster analysis of amino acids based on the frequecy counts data. Furthermore, Ward's clustering is also obtained by adopting the average score as an objective function. An ordinal cophenetic is introduced to compare results from different clustering methods.Comment: 6 pages, 1 figure, 6 table

Floquet Topological States in Shaking Optical Lattices

In this letter we propose realistic schemes to realize topologically nontrivial Floquet states by shaking optical lattices, using both one-dimension lattice and two-dimensional honeycomb lattice as examples. The topological phase in the two-dimensional model exhibits quantum anomalous Hall effect. The transition between topological trivial and nontrivial states can be easily controlled by shaking frequency and amplitude. Our schemes have two major advantages. First, both the static Hamiltonian and the shaking scheme are sufficiently simple to implement. Secondly, it requires relatively small shaking amplitude and therefore heating can be minimized. These two advantages make our scheme much more practical.Comment: 6 pages including supplementary materials, 3 figure

The QCD factorization in B→DKKB \to DKK decays

A study of hadron pair production mechanism is motivated by the recent observed decays $\bar B^0\to D^{(*)+}K^-K^0$. One novel phenomenon is threshold enhancement of the kaon pair production. We show that these decays in the heavy quark mass limit can be factorized into a generalized form. The new non-perturbative quantity is the generalized distribution amplitude which describes how a quark-antiquark pair transmits into the hadron pair. A proof of factorization of $\bar B^0\to D^{(*)+}K^-K^0$ decays to all-orders is performed by using the soft-collinear effective theory. The phenomenological application is discussed in brief.Comment: 14 pages, 2 figure

Quasi-particle Lifetime in a Mixture of Bose and Fermi Superfluids

In this letter, to reveal the effect of quasi-particle interactions in a Bose-Fermi superfluid mixture, we consider the lifetime of quasi-particle of Bose superfluid due to its interaction with quasi-particles in Fermi superfluid. We find that this damping rate, i.e. inverse of the lifetime, has quite different threshold behavior at the BCS and the BEC side of the Fermi superfluid. The damping rate is a constant nearby the threshold momentum in the BCS side, while it increases rapidly in the BEC side. This is because in the BCS side the decay processe is restricted by constant density-of-state of fermion quasi-particle nearby Fermi surface, while such a restriction does not exist in the BEC side where the damping process is dominated by bosonic quasi-particles of Fermi superfluid. Our results are related to collective mode experiment in recently realized Bose-Fermi superfluid mixture.Comment: 8 pages and 3 figures including supplemental materia

Vector Autoregressive POMDP Model Learning and Planning for Human-Robot Collaboration

Human-robot collaboration (HRC) has emerged as a hot research area at the intersection of control, robotics, and psychology in recent years. It is of critical importance to obtain an expressive but meanwhile tractable model for human beings in HRC. In this paper, we propose a model called Vector Autoregressive POMDP (VAR-POMDP) model which is an extension of the traditional POMDP model by considering the correlation among observations. The VAR-POMDP model is more powerful in the expressiveness of features than the traditional continuous observation POMDP since the traditional one is a special case of the VAR-POMDP model. Meanwhile, the proposed VAR-POMDP model is also tractable, as we show that it can be effectively learned from data and we can extend point-based value iteration (PBVI) to VAR-POMDP planning. Particularly, in this paper, we propose to use the Bayesian non-parametric learning to decide potential human states and learn a VAR-POMDP model using data collected from human demonstrations. Then, we consider planning with respect to PCTL which is widely used as safety and reachability requirement in robotics. Finally, the advantage of using the proposed model for HRC is validated by experimental results using data collected from a driver-assistance test-bed

A Simple Regularization-based Algorithm for Learning Cross-Domain Word Embeddings

Learning word embeddings has received a significant amount of attention recently. Often, word embeddings are learned in an unsupervised manner from a large collection of text. The genre of the text typically plays an important role in the effectiveness of the resulting embeddings. How to effectively train word embedding models using data from different domains remains a problem that is underexplored. In this paper, we present a simple yet effective method for learning word embeddings based on text from different domains. We demonstrate the effectiveness of our approach through extensive experiments on various down-stream NLP tasks.Comment: 7 pages, accepted by EMNLP 201

One-pass Person Re-identification by Sketch Online Discriminant Analysis

Person re-identification (re-id) is to match people across disjoint camera views in a multi-camera system, and re-id has been an important technology applied in smart city in recent years. However, the majority of existing person re-id methods are not designed for processing sequential data in an online way. This ignores the real-world scenario that person images detected from multi-cameras system are coming sequentially. While there is a few work on discussing online re-id, most of them require considerable storage of all passed data samples that have been ever observed, and this could be unrealistic for processing data from a large camera network. In this work, we present an onepass person re-id model that adapts the re-id model based on each newly observed data and no passed data are directly used for each update. More specifically, we develop an Sketch online Discriminant Analysis (SoDA) by embedding sketch processing into Fisher discriminant analysis (FDA). SoDA can efficiently keep the main data variations of all passed samples in a low rank matrix when processing sequential data samples, and estimate the approximate within-class variance (i.e. within-class covariance matrix) from the sketch data information. We provide theoretical analysis on the effect of the estimated approximate within-class covariance matrix. In particular, we derive upper and lower bounds on the Fisher discriminant score (i.e. the quotient between between-class variation and within-class variation after feature transformation) in order to investigate how the optimal feature transformation learned by SoDA sequentially approximates the offline FDA that is learned on all observed data. Extensive experimental results have shown the effectiveness of our SoDA and empirically support our theoretical analysis.Comment: Online learning, Person re-identification, Discriminant feature extractio

Core Influence Mechanism on Vertex-Cover Problem through Leaf-Removal-Core Breaking

Leaf-Removal process has been widely researched and applied in many mathematical and physical fields to help understand the complex systems, and a lot of problems including the minimal vertex-cover are deeply related to this process and the Leaf-Removal cores. In this paper, based on the structural features of the Leaf-Removal cores, a method named Core Influence is proposed to break the graphs into No-Leaf-Removal-Core ones, which takes advantages of identifying some significant nodes by localized and greedy strategy. By decomposing the minimal vertex-cover problem into the Leaf-Removal cores breaking process and maximal matching of the remained graphs, it is proved that any minimal vertex-covers of the whole graph can be located into these two processes, of which the latter one is a P problem, and the best boundary is achieved at the transition point. Compared with other node importance indices, the Core Influence method could break down the Leaf-Removal cores much faster and get the no-core graphs by removing fewer nodes from the graphs. Also, the vertex-cover numbers resulted from this method are lower than existing node importance measurements, and compared with the exact minimal vertex-cover numbers, this method performs appropriate accuracy and stability at different scales. This research provides a new localized greedy strategy to break the hard Leaf-Removal Cores efficiently and heuristic methods could be constructed to help understand some NP problems.Comment: 11pages, 6 figures, 2 table

Relatively Large Theta13 from Modification to the Tri-bimaximal, Bimaximal and Democratic Neutrino Mixing Matrices

Inspired by the recent T2K indication of a relatively large theta_{13}, we provide a systematic study of some general modifications to three mostly discussed neutrino mixing patterns, i.e., tri-bimaximal, bimaximal and democratic mixing matrices. The correlation between theta_{13} and two large mixing angles are provided according to each modifications. The phenomenological predictions of theta_{12} and theta_{23} are also discussed. After the exclusion of several minimal modifications, we still have reasonable predictions of three mixing angles in 3 Sigma level for other scenarios.Comment: 18 pages, 17 figure

Thermodynamic Properties of a Trapped Interacting Bose Gas

A Bose gas in an external potential is studied by means of the local density approximation. Analytical results are derived for the thermodynamic properties of an ideal Bose gas in a generic power-law trapping potential, and their dependence on the mutual interaction of atoms in the case of a non-ideal Bose gas.Comment: 10 pages, REVTex, no figure